质量改进方法
Strengthening education for sustainability through documented follow-up
Sets out a controlled approach to strengthening education for sustainability through documented follow-up, covering diagnosis, responsible action.
新闻与出版物
浏览国际教育质量认证委员会的新闻、标准更新和研究。
显示 12 条,共 43 条结果
已发布 1061 篇文章
质量改进方法
Sets out a controlled approach to strengthening education for sustainability through documented follow-up, covering diagnosis, responsible action.
数据与研究分析
Examines teacher supply and retention, addressing progress monitoring, source definitions, coverage, comparability, uncertainty and limits on inference.
标准解读
Explains the applicable evidential and assurance requirements in relation to institutional AI policies, covering scope, evidence, decision authority.
数据与研究分析
Examines learner agency, addressing reporting coverage and revision risk and the evidential limits relevant to responsible interpretation and decision-making.
质量改进方法
Sets out a staged improvement plan for teacher supply and retention, covering diagnosis, responsible action, outcome evidence and sustained effect.
质量改进方法
Sets out a staged improvement plan for implementation of AI literacy obligations, covering diagnosis, responsible action, outcome evidence and sustained effect.
标准解读
Explains the applicable evidential and assurance requirements in relation to human oversight in automated education decisions, covering scope, evidence, decision authority.
数据与研究分析
Examines automated decision oversight, addressing access, participation and outcomes, source definitions, coverage, comparability, uncertainty and limits on inference.
质量改进方法
Sets out a structured improvement method as an evidence-led approach to micro-credential quality, covering responsibility, outcome evidence and sustained effect.
质量改进方法
Sets out a controlled approach to improving ownership and follow-through for data minimisation, covering diagnosis, responsible action, outcome evidence and sustained effect.
政策与监管分析
Examines automated decision oversight through institutional responsibility, clarifying legal effect, institutional responsibility, learner safeguards and public-interest risk.
数据与研究分析
Examines implementation of AI literacy obligations, addressing timeliness and data completeness and the evidential limits relevant to responsible interpretation.